research-article

A semi-automatic tool for detection and tracking ground truth generation in videos

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C. SpampinatoAuthors Info & Claims

VIGTA '12: Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications

Article No.: 6, Pages 1 - 5

https://doi.org/10.1145/2304496.2304502

Published: 21 May 2012 Publication History

Abstract

In this paper we present a tool for the generation of ground-truth data for object detection, tracking and recognition applications. Compared to state of the art methods, such as ViPER-GT, our tool improves the user experience by providing edit shortcuts such as hotkeys and drag-and-drop, and by integrating computer vision algorithms to automate, under the supervision of the user, the extraction of contours and the identification of objects across frames. A comparison between our application and ViPER-GT tool was performed, which showed how our tool allows users to label a video in a shorter time, while at the same time providing a higher ground truth quality.

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Digital Library

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Digital Library

Cited By

Dahua LIsmail Z(2024)Multi-camera Large-Scale Intelligent Video AnalyticsProceedings of the 13th National Technical Seminar on Unmanned System Technology 2023—Volume 210.1007/978-981-97-2027-9_3(27-36)Online publication date: 17-Sep-2024
https://doi.org/10.1007/978-981-97-2027-9_3
Wilchek MHanley WLim JLuther KBatarseh F(2023)Human-in-the-loop for computer vision assuranceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106376123:PBOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106376
Mahmood MDiéz YOliver ASalvi JLladó X(2023)Motion-region annotation for complex videos via label propagation across occludersMachine Vision and Applications10.1007/s00138-022-01348-034:1Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s00138-022-01348-0
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Index Terms

A semi-automatic tool for detection and tracking ground truth generation in videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Other conferences

VIGTA '12: Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications

May 2012

74 pages

ISBN:9781450314053

DOI:10.1145/2304496

Editors:
Concetto Spampinato
University of Catania, Italy
,
Bas Boom
University of Edinburgh, UK
,
Jiyin He
Center of Mathematics & Informatics (CWI), the Netherlands

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 May 2012

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Seventh Framework Programme

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VIGTA '12

VIGTA '12: First International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications

May 21, 2012

Capri, Italy

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Overall Acceptance Rate 8 of 15 submissions, 53%

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Cited By

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https://doi.org/10.1007/978-981-97-2027-9_3
Wilchek MHanley WLim JLuther KBatarseh F(2023)Human-in-the-loop for computer vision assuranceEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106376123:PBOnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106376
Mahmood MDiéz YOliver ASalvi JLladó X(2023)Motion-region annotation for complex videos via label propagation across occludersMachine Vision and Applications10.1007/s00138-022-01348-034:1Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s00138-022-01348-0
Fernandez AFonseca PNina W(2023)Design of a Low-Cost RUV Stereo System for Monitoring of a Trout FarmIntelligent Computing10.1007/978-3-031-37963-5_73(1060-1069)Online publication date: 20-Aug-2023
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Li QLi JShi ZGu ZZheng HZheng BLi J(2021)A Holistic Marine Video DatasetOCEANS 2021: San Diego – Porto10.23919/OCEANS44145.2021.9705757(1-7)Online publication date: 20-Sep-2021
https://doi.org/10.23919/OCEANS44145.2021.9705757
Nair RDomnic S(2020)Underwater Video Compression Using Threshold Value Provided by Optimizing Algorithms2020 International Conference on Artificial Intelligence and Signal Processing (AISP)10.1109/AISP48273.2020.9073426(1-6)Online publication date: Jan-2020
https://doi.org/10.1109/AISP48273.2020.9073426
Yang LLiu YYu HFang XSong LLi DChen Y(2020)Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A ReviewArchives of Computational Methods in Engineering10.1007/s11831-020-09486-2Online publication date: 5-Sep-2020
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Gupta SMukherjee PChaudhury SLall B(2020)U-RME: Underwater Refined Motion Estimation in Hazy, Cluttered and Dynamic EnvironmentsComputer Vision, Pattern Recognition, Image Processing, and Graphics10.1007/978-981-15-8697-2_18(198-208)Online publication date: 17-Nov-2020
https://doi.org/10.1007/978-981-15-8697-2_18
da Silva JTabata ABroto LCocron MZimmer ABrandmeier T(2020)Open Source Multipurpose Multimedia Annotation ToolImage Analysis and Recognition10.1007/978-3-030-50347-5_31(356-367)Online publication date: 17-Jun-2020
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Yan ZDuckett TBellotto N(2019)Online learning for 3D LiDAR-based human detection: experimental analysis of point cloud clustering and classification methodsAutonomous Robots10.1007/s10514-019-09883-yOnline publication date: 12-Aug-2019
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